Thunderstorms are weather disturbances that can cause lightning, stormy winds, dense clouds, tornadoes, and heavy rain. Thunderstorms can cause extensive damage to people's lives, property, and economies, as well as livestock and national infrastructure. Early warning of thunderstorms can save people's lives and property. Previous thunderstorm prediction research did not develop a system for daily thunderstorm prediction with high accuracy for Bangladeshi citizens by assessing a wide range of meteorological variables. To address this issue, this work develops a daily high accuracy based localized thunderstorm event prediction system that analyzes various meteorological factors, dates, and specific location information. This dataset was analyzed using a variety of machine learning models, including traditional statistical models like ARMA, ARIMA, and SARIMA, as well as XGBoost ensemble methods and some deep learning models such as ANN, LSTM, and GRU. The results show that advanced neural network models, particularly GRU and LSTM, outperform others in terms of RMSE, R2, MAE, and MAPE. The GRU model outperformed all other schemes, with an RMSE of 0.794, R2 of 0.998, MAE of 0.476, and MAPE of 3.544%. The mobile application provides users with accurate, localized thunderstorm forecasts, allowing for better safety, event planning, and environmental preparedness. User feedback-based mobile app assessment confirms that more than 55% of users are highly satisfied with the thunderstorm assistance app’s features and usefulness.